Investigation into scalable and efficient enterotoxigenic Escherichia coli bacteriophage production
Bibliographic record
Abstract
Abstract As the demand for bacteriophage (phage) therapy increases due to antibiotic resistance in microbial pathogens, strategies and methods for increased efficiency, large-scale phage production need to be determined. To date, very little has been published on how to establish scalable production for phages, while achieving and maintaining a high titer in an economical manner. The present work outlines a phage production strategy using an enterotoxigenic Escherichia coli -targeting phage, ‘Phage75’, and accounts for the following variables: infection load, multiplicity of infection, temperature, media composition, harvest time, and host bacteria. To streamline this process, variables impacting phage propagation were screened through a high-throughput assay monitoring optical density at 600 nm (OD 600 ) to indirectly infer phage production from host cell lysis. Following screening, propagation conditions were translated in a scalable fashion in shake flasks at 0.01 L, 0.1 L, and 1 L. A final, proof-of-concept production was then carried out in a CellMaker bioreactor to represent practical application at an industrial level. Phage titers were obtained in the range of 9.5–10.1 log 10 PFU/mL with no significant difference between yields from shake flasks and CellMaker. Overall, this suggests that the methodology for scalable processing is reliable for translating into large-scale phage production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".